[1]} proposed a deep learning-based sinogram correction method to reduce the primary metal-induced beam-hardening factors along the metal trace in the sinogram. This method was applied in the restricted situation of a patient-implant-specific model in which two simple metallic objects were placed in the hip joint. [2]} proposed a dual-domain network (DuDoNet) to restore sinogram consistency and enhance CT images simultaneously. DuDoNet pursues MAR enhancement by leveraging dual-domain learning networks: sinogram and image enhancement networks. Here, the dual networks operate separately on the sinogram and image domains, but are trained in an end-to-end fashion. The sinogram enhancement network learns how to correct a metal-affected sinogram by using an inpainting loss on the metal trace and sinogram consistency loss. Since the final image is the output of the image enhancement network, the data fidelity may be compromised, resulting in anatomical structure changes. [3]} proposed a dual-domain joint learning network that first generates a good prior image with fewer metal artifacts; then, the forward projection of the prior image is utilized for sinogram enhancement. The final output is the reconstructed image from the sinogram modified only in the metal trace area.
